{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "from unidecode import unidecode\n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs = pd.read_csv('data/drake-songs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs = songs[songs['album'] == 'views']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140, 5)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0       int64\n",
       "Unnamed: 0.1     int64\n",
       "song            object\n",
       "album           object\n",
       "lyrics          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_songs(df, encoding_size=3):\n",
    "    words = []\n",
    "\n",
    "    for index, row in df['lyrics'].iteritems():\n",
    "        row = str(row).lower()\n",
    "        new_words = re.findall(r\"\\b[a-z']+[a-z']+\\b\", unidecode(row))\n",
    "        words = words + new_words\n",
    "\n",
    "    vocab = set(words)\n",
    "\n",
    "    word_to_index = {w: i for i, w in enumerate(vocab)}\n",
    "    index_to_word = {i: w for w, i in word_to_index.items()}\n",
    "    word_indices = [word_to_index[word] for word in vocab]\n",
    "    vocab_n = len(vocab)\n",
    "    \n",
    "    print('vocabulary size: {}'.format(vocab_n))\n",
    "\n",
    "    ntk = [vocab_n]\n",
    "    X_indices_of_ones = np.array(word_indices + word_indices)\n",
    "    previous_word = ntk + word_indices[:-1]\n",
    "    next_word = word_indices[1:] + ntk\n",
    "    y_indices_of_ones = np.array(previous_word + next_word)\n",
    "\n",
    "    X = np.zeros((X_indices_of_ones.shape[0], vocab_n + 1))\n",
    "    X[np.arange(X.shape[0]), X_indices_of_ones] = 1\n",
    "\n",
    "    y = np.zeros((y_indices_of_ones.shape[0], vocab_n + 1))\n",
    "    y[np.arange(y.shape[0]), y_indices_of_ones] = 1\n",
    "\n",
    "    batch_size = 10\n",
    "    divisible_size = int(vocab_n / 10) * 10\n",
    "\n",
    "    X_b = X[:divisible_size, :].reshape(int(divisible_size / batch_size), batch_size, vocab_n + 1)\n",
    "    y_b = y[:divisible_size, :].reshape(int(divisible_size / batch_size), batch_size, vocab_n + 1)\n",
    "\n",
    "    print('number of batches: {}'.format(divisible_size/batch_size))\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Dense(encoding_size, input_shape=(batch_size, vocab_n + 1), activation='relu'))\n",
    "    model.add(Dense(vocab_n + 1, activation='softmax'))\n",
    "    model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    return model, word_to_index, word_indices, index_to_word, X_b, y_b, vocab_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing all\n",
      "vocabulary size: 5766\n",
      "number of batches: 576.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# albums = set(songs['album'].values)\n",
    "albums = ['all']\n",
    "album_models = {}\n",
    "album_word_to_index = {}\n",
    "album_word_indices = {}\n",
    "album_index_to_word = {}\n",
    "album_vocab_n = {}\n",
    "album_Xs = {}\n",
    "album_ys = {}\n",
    "\n",
    "for album in albums:\n",
    "    print('processing {}'.format(album))\n",
    "    if album == 'all':\n",
    "        df = songs\n",
    "    else:\n",
    "        df = songs[songs['album'] == album]\n",
    "\n",
    "    album_models[album], album_word_to_index[album], album_word_indices[album], album_index_to_word[album], album_Xs[album], album_ys[album], album_vocab_n[album] = process_songs(df)\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting: all\n",
      "Epoch 1/300\n",
      "576/576 [==============================] - 5s 9ms/step - loss: 8.6636 - acc: 0.0000e+00\n",
      "Epoch 2/300\n",
      "576/576 [==============================] - 5s 8ms/step - loss: 8.6606 - acc: 1.7361e-04\n",
      "Epoch 3/300\n",
      "576/576 [==============================] - 5s 8ms/step - loss: 8.6600 - acc: 3.4722e-04\n",
      "Epoch 4/300\n",
      "576/576 [==============================] - 5s 9ms/step - loss: 8.6592 - acc: 5.2083e-04\n",
      "Epoch 5/300\n",
      "576/576 [==============================] - 5s 9ms/step - loss: 8.6582 - acc: 1.7361e-04\n",
      "Epoch 6/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6569 - acc: 1.7361e-04\n",
      "Epoch 7/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6552 - acc: 1.7361e-04\n",
      "Epoch 8/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.6532 - acc: 3.4722e-04\n",
      "Epoch 9/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6508 - acc: 1.7361e-04\n",
      "Epoch 10/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6478 - acc: 1.7361e-04\n",
      "Epoch 11/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6444 - acc: 1.7361e-04\n",
      "Epoch 12/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6405 - acc: 1.7361e-04\n",
      "Epoch 13/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6360 - acc: 3.4722e-04\n",
      "Epoch 14/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6310 - acc: 1.7361e-04\n",
      "Epoch 15/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6254 - acc: 1.7361e-04\n",
      "Epoch 16/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6193 - acc: 3.4722e-04\n",
      "Epoch 17/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6127 - acc: 3.4722e-04\n",
      "Epoch 18/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.6056 - acc: 1.7361e-04\n",
      "Epoch 19/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5979 - acc: 1.7361e-04\n",
      "Epoch 20/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5899 - acc: 1.7361e-04\n",
      "Epoch 21/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5813 - acc: 1.7361e-04\n",
      "Epoch 22/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5722 - acc: 3.4722e-04\n",
      "Epoch 23/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5628 - acc: 5.2083e-04\n",
      "Epoch 24/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5529 - acc: 6.9444e-04\n",
      "Epoch 25/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5427 - acc: 0.0012\n",
      "Epoch 26/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5320 - acc: 5.2083e-04\n",
      "Epoch 27/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5210 - acc: 1.7361e-04\n",
      "Epoch 28/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.5098 - acc: 6.9444e-04\n",
      "Epoch 29/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.4982 - acc: 6.9444e-04\n",
      "Epoch 30/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.4862 - acc: 6.9444e-04\n",
      "Epoch 31/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.4742 - acc: 3.4722e-04\n",
      "Epoch 32/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.4618 - acc: 3.4722e-04\n",
      "Epoch 33/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.4492 - acc: 3.4722e-04\n",
      "Epoch 34/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.4364 - acc: 1.7361e-04\n",
      "Epoch 35/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.4236 - acc: 6.9444e-04\n",
      "Epoch 36/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.4105 - acc: 6.9444e-04\n",
      "Epoch 37/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.3973 - acc: 5.2083e-04\n",
      "Epoch 38/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.3841 - acc: 6.9444e-04\n",
      "Epoch 39/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.3708 - acc: 8.6806e-04\n",
      "Epoch 40/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.3574 - acc: 0.0010\n",
      "Epoch 41/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.3437 - acc: 8.6806e-04\n",
      "Epoch 42/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.3302 - acc: 6.9444e-04\n",
      "Epoch 43/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.3167 - acc: 6.9444e-04\n",
      "Epoch 44/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.3032 - acc: 5.2083e-04\n",
      "Epoch 45/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.2896 - acc: 5.2083e-04\n",
      "Epoch 46/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.2760 - acc: 5.2083e-04\n",
      "Epoch 47/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.2626 - acc: 3.4722e-04\n",
      "Epoch 48/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.2492 - acc: 6.9444e-04\n",
      "Epoch 49/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.2357 - acc: 8.6806e-04\n",
      "Epoch 50/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.2223 - acc: 6.9444e-04\n",
      "Epoch 51/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.2090 - acc: 8.6806e-04\n",
      "Epoch 52/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.1959 - acc: 8.6806e-04\n",
      "Epoch 53/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.1827 - acc: 8.6806e-04\n",
      "Epoch 54/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.1697 - acc: 0.0010\n",
      "Epoch 55/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.1569 - acc: 8.6806e-04\n",
      "Epoch 56/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.1440 - acc: 8.6806e-04\n",
      "Epoch 57/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.1312 - acc: 8.6806e-04\n",
      "Epoch 58/300\n",
      "576/576 [==============================] - 6s 10ms/step - loss: 8.1185 - acc: 8.6806e-04\n",
      "Epoch 59/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.1059 - acc: 3.4722e-04\n",
      "Epoch 60/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.0935 - acc: 0.0014\n",
      "Epoch 61/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.0812 - acc: 8.6806e-04\n",
      "Epoch 62/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.0691 - acc: 8.6806e-04\n",
      "Epoch 63/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.0570 - acc: 6.9444e-04\n",
      "Epoch 64/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.0449 - acc: 6.9444e-04\n",
      "Epoch 65/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.0332 - acc: 6.9444e-04\n",
      "Epoch 66/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.0214 - acc: 5.2083e-04\n",
      "Epoch 67/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 8.0099 - acc: 6.9444e-04\n",
      "Epoch 68/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9982 - acc: 5.2083e-04\n",
      "Epoch 69/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9868 - acc: 3.4722e-04\n",
      "Epoch 70/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9755 - acc: 6.9444e-04\n",
      "Epoch 71/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9644 - acc: 5.2083e-04\n",
      "Epoch 72/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9533 - acc: 5.2083e-04\n",
      "Epoch 73/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9425 - acc: 6.9444e-04\n",
      "Epoch 74/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9315 - acc: 6.9444e-04\n",
      "Epoch 75/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9209 - acc: 1.7361e-04\n",
      "Epoch 76/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.9102 - acc: 8.6806e-04\n",
      "Epoch 77/300\n",
      "576/576 [==============================] - 7s 11ms/step - loss: 7.8997 - acc: 6.9444e-04\n",
      "Epoch 78/300\n",
      "576/576 [==============================] - 7s 11ms/step - loss: 7.8893 - acc: 6.9444e-04\n",
      "Epoch 79/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8790 - acc: 8.6806e-04\n",
      "Epoch 80/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8688 - acc: 6.9444e-04\n",
      "Epoch 81/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8588 - acc: 5.2083e-04\n",
      "Epoch 82/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8488 - acc: 8.6806e-04\n",
      "Epoch 83/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8390 - acc: 0.0010\n",
      "Epoch 84/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8293 - acc: 8.6806e-04\n",
      "Epoch 85/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8197 - acc: 3.4722e-04\n",
      "Epoch 86/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8101 - acc: 8.6806e-04\n",
      "Epoch 87/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.8005 - acc: 5.2083e-04\n",
      "Epoch 88/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7912 - acc: 5.2083e-04\n",
      "Epoch 89/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7820 - acc: 1.7361e-04\n",
      "Epoch 90/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7729 - acc: 3.4722e-04\n",
      "Epoch 91/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7639 - acc: 5.2083e-04\n",
      "Epoch 92/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7549 - acc: 5.2083e-04\n",
      "Epoch 93/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7458 - acc: 6.9444e-04\n",
      "Epoch 94/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7371 - acc: 1.7361e-04\n",
      "Epoch 95/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7282 - acc: 5.2083e-04\n",
      "Epoch 96/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7197 - acc: 6.9444e-04\n",
      "Epoch 97/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7112 - acc: 8.6806e-04\n",
      "Epoch 98/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.7027 - acc: 6.9444e-04\n",
      "Epoch 99/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6945 - acc: 8.6806e-04\n",
      "Epoch 100/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6861 - acc: 5.2083e-04\n",
      "Epoch 101/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6778 - acc: 3.4722e-04\n",
      "Epoch 102/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6699 - acc: 8.6806e-04\n",
      "Epoch 103/300\n",
      "576/576 [==============================] - 7s 11ms/step - loss: 7.6618 - acc: 5.2083e-04\n",
      "Epoch 104/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6538 - acc: 5.2083e-04\n",
      "Epoch 105/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6459 - acc: 8.6806e-04\n",
      "Epoch 106/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6381 - acc: 5.2083e-04\n",
      "Epoch 107/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6304 - acc: 6.9444e-04\n",
      "Epoch 108/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6227 - acc: 6.9444e-04\n",
      "Epoch 109/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6151 - acc: 3.4722e-04\n",
      "Epoch 110/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6077 - acc: 5.2083e-04\n",
      "Epoch 111/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.6004 - acc: 6.9444e-04\n",
      "Epoch 112/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5930 - acc: 5.2083e-04\n",
      "Epoch 113/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5857 - acc: 5.2083e-04\n",
      "Epoch 114/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5785 - acc: 1.7361e-04\n",
      "Epoch 115/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5713 - acc: 6.9444e-04\n",
      "Epoch 116/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5643 - acc: 5.2083e-04\n",
      "Epoch 117/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5573 - acc: 5.2083e-04\n",
      "Epoch 118/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5504 - acc: 8.6806e-04\n",
      "Epoch 119/300\n",
      "576/576 [==============================] - 7s 11ms/step - loss: 7.5436 - acc: 6.9444e-04\n",
      "Epoch 120/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5368 - acc: 6.9444e-04\n",
      "Epoch 121/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5300 - acc: 6.9444e-04\n",
      "Epoch 122/300\n",
      "576/576 [==============================] - 6s 11ms/step - loss: 7.5234 - acc: 5.2083e-04\n",
      "Epoch 123/300\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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      "576/576 [==============================] - 7s 13ms/step - loss: 6.9225 - acc: 0.0030\n",
      "Epoch 297/300\n",
      "576/576 [==============================] - 8s 13ms/step - loss: 6.9209 - acc: 0.0026\n",
      "Epoch 298/300\n",
      "576/576 [==============================] - 8s 13ms/step - loss: 6.9192 - acc: 0.0024\n",
      "Epoch 299/300\n",
      "576/576 [==============================] - 7s 13ms/step - loss: 6.9175 - acc: 0.0026\n",
      "Epoch 300/300\n",
      "576/576 [==============================] - 8s 13ms/step - loss: 6.9159 - acc: 0.0024\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ef8cac048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "album_weights = {}\n",
    "album_histories = {}\n",
    "\n",
    "for album in albums:\n",
    "    print('fitting: {}'.format(album))\n",
    "    model = album_models[album]\n",
    "    album_histories[album] = model.fit(album_ys[album], album_Xs[album], epochs=300)\n",
    "    album_weights[album] = model.layers[0].get_weights()[0]\n",
    "    plt.plot(history.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def x_is_to_y_as_z_is_to(x, y, z, num_closest, word_to_index, encoding):\n",
    "    x_v = encoding[word_to_index[x]]\n",
    "    y_v = encoding[word_to_index[y]]\n",
    "    z_v = encoding[word_to_index[z]]\n",
    "    \n",
    "    relationship_vector = x_v - y_v\n",
    "    z_is_to = z_v + relationship_vector\n",
    "    \n",
    "    return closest_word_to_vector(z_is_to, num_closest)\n",
    "\n",
    "x_is_to_y_as_z_is_to(\"big\", \"rings\", \"america\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def build_pred_model(vocab_n, encoding_size=8):\n",
    "    prediction_model = Sequential()\n",
    "    prediction_model.add(Dense(encoding_size, input_dim=vocab_n + 1, activation='relu'))\n",
    "    prediction_model.add(Dense(vocab_n + 1, activation='softmax'))\n",
    "    prediction_model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    prediction_model.set_weights(model.get_weights())\n",
    "\n",
    "def words_likely_to_be_in_the_context_of(word, num_context, vocab_n):\n",
    "    input_vector = np.zeros((1, vocab_n + 1))\n",
    "    input_vector[0, word_to_index[word]] = 1\n",
    "    output_vector = prediction_model.predict(input_vector)[0]\n",
    "    top_context = np.argsort(-output_vector)[:num_context]\n",
    "    return [index_to_word[i] for i in top_context]\n",
    "\n",
    "words_likely_to_be_in_the_context_of('america', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_similarity(u, v):\n",
    "    distance = 0.0\n",
    "    dot = np.dot(u, v)\n",
    "    norm_u = np.linalg.norm(u)\n",
    "    \n",
    "    norm_v = np.linalg.norm(v)\n",
    "    cosine_similarity = dot / (norm_u * norm_v)\n",
    "    \n",
    "    return cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_similarity(encoding[word_to_index['school']], encoding[word_to_index['ignore']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attempting TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_tsne(encoding, index_to_word):\n",
    "    X_embedded = TSNE(n_components=2, perplexity=100).fit_transform(encoding)\n",
    "    \n",
    "    plt.figure(figsize=(20, 10))\n",
    "\n",
    "    for i in range(0, len(X_embedded)):\n",
    "        plt.scatter(X_embedded[i, 0], X_embedded[i, 1], s=50)\n",
    "\n",
    "        if (i % 1 == 0):\n",
    "            plt.annotate(\n",
    "                index_to_word[i], \n",
    "                (\n",
    "                    X_embedded[i, 0], \n",
    "                    X_embedded[i, 1]\n",
    "                ), \n",
    "                bbox=dict(boxstyle=\"round\", \n",
    "                          fc=(1.0, 0.7, 0.7),\n",
    "                          ec=(1., .5, .5)), \n",
    "                fontsize=15\n",
    "            )\n",
    "\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plot_tsne(album_weights['thank-me-later'], album_index_to_word['thank-me-later'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3D plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpl_toolkits.mplot3d.axes3d.Axes3D"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5767, 3)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "album_weights[album].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'all'"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "album"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "interesting_words = [\n",
    "    'lover', \n",
    "    'relationships',\n",
    "    'friendships',\n",
    "    'friendship',\n",
    "    'sex',\n",
    "    'bitches', \n",
    "    'gun', \n",
    "    'violent',\n",
    "    'fight',\n",
    "    'liquor', \n",
    "    'henny', \n",
    "    'girl',\n",
    "    'lady',\n",
    "    'mom',\n",
    "    'strippers',\n",
    "    'stripper',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "interesting_words = ['liquor',\n",
    " 'stop',\n",
    " 'you',\n",
    " 'yours',\n",
    " 'airin',\n",
    " 'energy',\n",
    " 'fit',\n",
    " 'soldier',\n",
    " 'games',\n",
    " 'young',\n",
    " 'morals',\n",
    " 'history',\n",
    " 'hall',\n",
    " 'writer',\n",
    " 'free']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_3d(weights, index_to_word):\n",
    "    fig = plt.figure(figsize=(20, 10))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "    for i in range(0, len(weights) - 1):\n",
    "        ax.scatter(\n",
    "            weights[i, 0], \n",
    "            weights[i, 1], \n",
    "            weights[i, 2],\n",
    "            s=75,\n",
    "        )\n",
    "        \n",
    "        word = index_to_word[i]\n",
    "        \n",
    "        if (word in interesting_words):\n",
    "            random = np.random.randn(2)\n",
    "            x = random[0] / 5\n",
    "            y = random[1] / 5\n",
    "            ax.text(weights[i, 0] + x, weights[i, 1] + y, weights[i, 2], word, size=12, zorder=0, color='k')\n",
    "\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ef6b02080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for album in album_weights:\n",
    "    plot_3d(album_weights[album], album_index_to_word[album])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wri',\n",
       " 'competition',\n",
       " 'followin',\n",
       " 'bands',\n",
       " 'guala',\n",
       " 'briefcase',\n",
       " 'calamari',\n",
       " 'ting',\n",
       " 'sales',\n",
       " 'invite',\n",
       " 'ross',\n",
       " 'three',\n",
       " 'quarters',\n",
       " 'here',\n",
       " 'psychic',\n",
       " 'ym',\n",
       " 'wife',\n",
       " 'young',\n",
       " 'supposed',\n",
       " 'olive',\n",
       " 'taki',\n",
       " 'potluck',\n",
       " 'meanor',\n",
       " 'hate',\n",
       " 'purpose']"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def closest_word_to_vector(album, word_vector, num_nearby):\n",
    "    distances = np.linalg.norm(album_weights[album] - word_vector, axis=1)\n",
    "    closest = np.argsort(distances)[:num_nearby]\n",
    "    return [album_index_to_word[album][i] for i in closest[1:]]\n",
    "\n",
    "def closest_word(album, word, num_nearby):\n",
    "    ind = album_word_to_index[album][word]\n",
    "    word_vector = album_weights[album][ind]\n",
    "    return closest_word_to_vector(album, word_vector, num_nearby + 1)\n",
    "\n",
    "closest_word('all', 'energy', 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
